Data Visualization with R

Workshop goals

• learn about various packages for making data visualizations in R
• be able to determine which package to use
• understand basic syntax for the popular packages
• learn basic design principles for effective communication
• gain hands-on practice making different visualization types

What is R?

R is a software environment for statistical computing and graphics. Using R you can do rigorous statistical analysis, clean and manipulate data, and create publication-quality graphics.

clustering map

Popularity of R

Source: Stephen Cass, “The 2016 Top Programming Langauges”, IEEE Spectrum

Popular R packages for data viz

• ggplot2
• highcharter
• leaflet
• plotly

ggplot2

Source:

highcharter

data(citytemp)

hc <- highchart() %>% 
  hc_xAxis(categories = citytemp$month) %>% 
  hc_add_series(name = "Tokyo", data = citytemp$tokyo) %>% 
  hc_add_series(name = "London", data = citytemp$london) %>% 
  hc_add_series(name = "Other city",
                data = (citytemp$tokyo + citytemp$london)/2)

hc

leaflet

m <- leaflet() %>%
  addTiles() %>%  # Add default OpenStreetMap map tiles
  addMarkers(lng=-78.6697, lat=35.7876,
    popup="You are here")
m  # Print the map

plotly

p <- plot_ly(economics, x = ~date, y = ~unemploy / pop)
p

Deciding on the right package

• Do you want your visualizations to be interactive or static?
• interactive: plotly, highcharter, ggviz
• static: ggplot2
• Do you want to use R Markdown/knitr to publish your visualization?
• not ggvis (it)
• Do you want to create a dashboard for multiple vizzes?
• Are you creating your visualization for a commercial or government purpose?
• Do you need to do something sophisticated with geospatial data?